Due to the multi-output nature of both CoQA and QuAC, multi-task training is quite common amongst CMC models, e.g. HAM [1]} uses multi-task learning over QuAC to also predict dialog prediction and continuation acts, while GraphFlow [2]} uses multi-task learning over CoQA to also predict question type. Besides, recently published [3]} achieved state-of-the-art results using RoBERTa, by applying multiple training techniques over CoQA. These consist of rationale tagging multi-task learning (predict if the token exists in CoQA evidence), Adversarial Training [4]}, and Knowledge Distillation [5]}.
